dataset_infoGet metadata for one Brussels Open Data dataset (fields/schema, themes, record count) — call before query to learn the column names.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Dataset id from search_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, etc. Description adds context by specifying returned metadata (fields/schema, themes, record count), which is useful beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, well-structured sentence that front-loads the purpose and includes a usage tip. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has one parameter and no output schema. Description adequately explains what metadata is returned (fields/schema, themes, record count), making it complete for an agent to understand behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. Description only reiterates that dataset_id comes from search_datasets, adding no new meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states: gets metadata (fields/schema, themes, record count) for a Brussels Open Data dataset. It distinguishes from siblings like 'search_datasets' (finding datasets) and 'query' (requesting data) by specifying 'call before query to learn column names'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'call before query to learn the column names', indicating when to use. Does not explicitly state when not to use or name alternatives, but context is clear given sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, with clear descriptions differentiating similar ones like ask_pipeworx and ask_pipeworx_grounded. However, some overlap exists between deep_research and ask_pipeworx, though descriptions provide guidance.
Tool names are inconsistent, mixing snake_case (ai_visibility_check), multi-word phrases (scan_competitor_ai_presence), and simple verbs (query, recall). No uniform pattern like verb_noun convention.
33 tools is on the high side, but the server covers a broad domain (data querying, prediction markets, subscriptions). It feels slightly heavy but still manageable; borderline between reasonable and excessive.
The Pipeworx and prediction market tools are comprehensive, but Brussels Open Data is underrepresented with only three tools (query, dataset_info, search_datasets). Missing update/delete operations for Brussels data, though likely read-only.